Establishing Distributed Hidden Friendship Relations

(Transcript of Discussion)
  • Sören Preibusch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7028)


Welcome to the penultimate talk of the workshop. I will be talking today about hidden friendship relations, and I will introduce you to the concept of what hidden friendship relations are, say why we need them, and I will also explain how we can achieve them.

I will do so with the perspective on privacy, and focusing on distributed architectures. What has come up during the past few days is social networking in all of the scenarios that we have seen, and all the applications that can be run on these, and experiments we can do in social networks. Here you can see a screenshot of social network, it’s a profile page for Friendster, and besides the fact that there’s this nice guy, we have his friends over here, and friendship relations are the underlying relations that build a social network, so it can be used as a core to build higher level concepts. It’s simple, but it’s formalised, you can easily crawl it and so forth. There’s also an interesting function property about friendship, that it’s symmetric, so here we have Gwen, and if we browse to Gwen’s profile page and look up her friends we see that Hennesey is again there. So we have the symmetry that is enforced across the network, that’s an important property about friendship relations. Another thing is that they carry the property, to allow privileged actions, so if I have someone, you look over there to the left you can see that you can forward the profile page to one of your own friends but you can’t forward their profile page to other friends. So this privileged action that is based on friendship can also have privileges that are with regard to resources such as hiding some things in the profile. This is what I will call the positive privacy of friendship relations.


High Level Concept Friendship Relation Public List Mobile Social Network Broadcast Encryption 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sören Preibusch
    • 1
  1. 1.University of CambridgeUK

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